Big Data

Unit code: NIT2202 | Study level: Undergraduate
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Footscray Park
Online Real Time
VU Sydney
NIT1102 - Introduction to Programming; or
NIT1201 - Introduction to Database Systems
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

'Big Data' phenomenon is an emerging force in the global business world. It is characterised by five Vs: Volume, Velocity, Variety, Veracity and Value. It increasingly makes data sets too large to store and analyse beyond the ability of traditional relational database technology. This unit provides fundamentals related to the technology and the core concepts behind big data problems, applications, and systems. It provides an introduction to the most common open-source software framework to increase the potential for data to transform our world. Students will develop comprehensive understanding of the challenges that organisations are facing for managing 'Big Data' and the technological solutions for efficient and strategic decision making.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Analyse and illustrate Big Data challenges to the business world;
  2. Explain the impact of Big Data's five V's (volume, velocity, variety veracity and value) using real world examples;
  3. Apply architectural components and programming models of commonly used Big Data;
  4. Create a technological solution using open-source software framework.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Test 1 (1 hour theoretical knowledge test)
Assessment type: Test
|
Grade: 20%
Test 2 (1 hour theoretical knowledge test)
Assessment type: Laboratory Work
|
Grade: 30%
Practical Lab work
Assessment type: Case Study
|
Grade: 30%
Group assignment and presentation

Other locations

For students studying at Henan University
Assessment type: Case Study
|
Grade: 25%
Group assignment and presentation
Assessment type: Laboratory Work
|
Grade: 25%
Weekly Practical Lab Work
Assessment type: Examination
|
Grade: 50%
Final Written Examination (3 hours)

Required reading

Big data fundamentals: Concepts, drivers & techniques
Erl, T., Khattak, W., & Buhler. P. (2016)| Prentice Hall: Boston, MA
Hands-On Data Science with Anaconda
Yan, Y. & Yan, J. (2018)| Packt Publishing.

As part of a course

This unit is studied as part of the following course(s):

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